TY - JOUR
T1 - A new spatial regression estimator in the multivariate context
AU - Dabo-Niang, Sophie
AU - Ternynck, Camille
AU - Yao, Anne Francoise
N1 - Publisher Copyright:
© 2015 Académie des sciences.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - In this note, we propose a nonparametric spatial estimator of the regression function x→r(x):=E[Yi|Xi=x],x∈Rd, of a stationary (d+1)-dimensional spatial process {(Yi,Xi),i∈ZN}, at a point located at some station j. The proposed estimator depends on two kernels in order to control both the distance between observations and the spatial locations. Almost complete convergence and consistency in Lq norm (q∈N*) of the kernel estimate are obtained when the sample considered is an α-mixing sequence.
AB - In this note, we propose a nonparametric spatial estimator of the regression function x→r(x):=E[Yi|Xi=x],x∈Rd, of a stationary (d+1)-dimensional spatial process {(Yi,Xi),i∈ZN}, at a point located at some station j. The proposed estimator depends on two kernels in order to control both the distance between observations and the spatial locations. Almost complete convergence and consistency in Lq norm (q∈N*) of the kernel estimate are obtained when the sample considered is an α-mixing sequence.
U2 - 10.1016/j.crma.2015.04.004
DO - 10.1016/j.crma.2015.04.004
M3 - Article
AN - SCOPUS:84930417904
SN - 1631-073X
VL - 353
SP - 635
EP - 639
JO - Comptes Rendus Mathematique
JF - Comptes Rendus Mathematique
IS - 7
ER -